Inference, Estimation, and Learning

3 credits · College of Engineering catalogue

Advisory and Enforced Prerequisite: None. (3 credits) Theory and algorithms for synthesizing models and data for general applications across science and engineering. Topics include algorithms for maximum likelihood estimation, Bayesian inference, and regression for static inference problems and for estimation in dynamical systems. Theoretical foundations of the algorithms and projects that focus on implementation.